Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations21103
Missing cells566
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.2 MiB
Average record size in memory308.2 B

Variable types

Categorical4
Numeric13

Alerts

ACType is highly overall correlated with AC_role and 1 other fieldsHigh correlation
AC_role is highly overall correlated with ACTypeHigh correlation
airGroundVector_groundSpeed is highly overall correlated with calculated_timeHigh correlation
calculated_time is highly overall correlated with airGroundVector_groundSpeed and 2 other fieldsHigh correlation
distapt is highly overall correlated with calculated_time and 2 other fieldsHigh correlation
dwpt is highly overall correlated with tempHigh correlation
emitterCat is highly overall correlated with ACTypeHigh correlation
h3_id is highly overall correlated with distaptHigh correlation
hrstart is highly overall correlated with rhum and 1 other fieldsHigh correlation
rhum is highly overall correlated with hrstart and 2 other fieldsHigh correlation
temp is highly overall correlated with dwpt and 2 other fieldsHigh correlation
ttland is highly overall correlated with calculated_time and 1 other fieldsHigh correlation
wspd is highly overall correlated with rhumHigh correlation
AC_role is highly imbalanced (89.3%)Imbalance
dwpt has 234 (1.1%) missing valuesMissing
rhum has 234 (1.1%) missing valuesMissing
ttland has unique valuesUnique
hrstart has 1153 (5.5%) zerosZeros
wdir has 873 (4.1%) zerosZeros
wspd has 839 (4.0%) zerosZeros

Reproduction

Analysis started2024-10-09 19:06:24.690179
Analysis finished2024-10-09 19:07:15.629421
Duration50.94 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

ACType
Categorical

HIGH CORRELATION 

Distinct36
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
E175
3575 
A321-200
2631 
B737-800
2156 
B737-8
1760 
B737-700
1558 
Other values (31)
9423 

Length

Max length11
Median length8
Mean length7.1028764
Min length4

Characters and Unicode

Total characters149892
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA321-200
2nd rowE175
3rd rowA321-200
4th rowA320-200
5th rowB737-700

Common Values

ValueCountFrequency (%)
E175 3575
16.9%
A321-200 2631
12.5%
B737-800 2156
10.2%
B737-8 1760
8.3%
B737-700 1558
 
7.4%
A320-200 1547
 
7.3%
B737-900 1381
 
6.5%
B777-300 879
 
4.2%
B787-9 735
 
3.5%
B737-9 542
 
2.6%
Other values (26) 4339
20.6%

Length

2024-10-09T15:07:15.926217image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
e175 3575
16.6%
a321-200 2631
12.2%
b737-800 2156
10.0%
b737-8 1760
 
8.2%
b737-700 1558
 
7.2%
a320-200 1547
 
7.2%
b737-900 1381
 
6.4%
b777-300 879
 
4.1%
b787-9 735
 
3.4%
b737-9 542
 
2.5%
Other values (27) 4734
22.0%

Most occurring characters

ValueCountFrequency (%)
0 31008
20.7%
7 27154
18.1%
- 17703
11.8%
3 15421
10.3%
B 10539
 
7.0%
2 9740
 
6.5%
1 7884
 
5.3%
8 6134
 
4.1%
A 6072
 
4.1%
5 4646
 
3.1%
Other values (16) 13591
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 149892
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 31008
20.7%
7 27154
18.1%
- 17703
11.8%
3 15421
10.3%
B 10539
 
7.0%
2 9740
 
6.5%
1 7884
 
5.3%
8 6134
 
4.1%
A 6072
 
4.1%
5 4646
 
3.1%
Other values (16) 13591
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 149892
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 31008
20.7%
7 27154
18.1%
- 17703
11.8%
3 15421
10.3%
B 10539
 
7.0%
2 9740
 
6.5%
1 7884
 
5.3%
8 6134
 
4.1%
A 6072
 
4.1%
5 4646
 
3.1%
Other values (16) 13591
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 149892
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 31008
20.7%
7 27154
18.1%
- 17703
11.8%
3 15421
10.3%
B 10539
 
7.0%
2 9740
 
6.5%
1 7884
 
5.3%
8 6134
 
4.1%
A 6072
 
4.1%
5 4646
 
3.1%
Other values (16) 13591
9.1%

AC_role
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
PAX
20515 
Not Found
 
395
CAR
 
168
VIP
 
25

Length

Max length9
Median length3
Mean length3.1123063
Min length3

Characters and Unicode

Total characters65679
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPAX
2nd rowPAX
3rd rowPAX
4th rowPAX
5th rowPAX

Common Values

ValueCountFrequency (%)
PAX 20515
97.2%
Not Found 395
 
1.9%
CAR 168
 
0.8%
VIP 25
 
0.1%

Length

2024-10-09T15:07:16.305645image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-09T15:07:16.627964image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
pax 20515
95.4%
not 395
 
1.8%
found 395
 
1.8%
car 168
 
0.8%
vip 25
 
0.1%

Most occurring characters

ValueCountFrequency (%)
A 20683
31.5%
P 20540
31.3%
X 20515
31.2%
o 790
 
1.2%
N 395
 
0.6%
t 395
 
0.6%
395
 
0.6%
F 395
 
0.6%
u 395
 
0.6%
n 395
 
0.6%
Other values (5) 781
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 65679
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 20683
31.5%
P 20540
31.3%
X 20515
31.2%
o 790
 
1.2%
N 395
 
0.6%
t 395
 
0.6%
395
 
0.6%
F 395
 
0.6%
u 395
 
0.6%
n 395
 
0.6%
Other values (5) 781
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 65679
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 20683
31.5%
P 20540
31.3%
X 20515
31.2%
o 790
 
1.2%
N 395
 
0.6%
t 395
 
0.6%
395
 
0.6%
F 395
 
0.6%
u 395
 
0.6%
n 395
 
0.6%
Other values (5) 781
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 65679
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 20683
31.5%
P 20540
31.3%
X 20515
31.2%
o 790
 
1.2%
N 395
 
0.6%
t 395
 
0.6%
395
 
0.6%
F 395
 
0.6%
u 395
 
0.6%
n 395
 
0.6%
Other values (5) 781
 
1.2%

ttland
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21103
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.963119
Minimum30.204979
Maximum106.74826
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size165.0 KiB
2024-10-09T15:07:17.109840image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum30.204979
5-th percentile33.232191
Q135.806494
median39.832966
Q343.098615
95-th percentile48.105207
Maximum106.74826
Range76.543286
Interquartile range (IQR)7.2921209

Descriptive statistics

Standard deviation4.8528809
Coefficient of variation (CV)0.12143399
Kurtosis3.3570873
Mean39.963119
Median Absolute Deviation (MAD)3.6808573
Skewness0.80403349
Sum843341.7
Variance23.550453
MonotonicityNot monotonic
2024-10-09T15:07:17.484262image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39.58688199 1
 
< 0.1%
38.15249364 1
 
< 0.1%
32.63368179 1
 
< 0.1%
37.65359716 1
 
< 0.1%
39.16423423 1
 
< 0.1%
32.1277138 1
 
< 0.1%
33.60764008 1
 
< 0.1%
33.29120458 1
 
< 0.1%
33.14425843 1
 
< 0.1%
33.75548267 1
 
< 0.1%
Other values (21093) 21093
> 99.9%
ValueCountFrequency (%)
30.20497925 1
< 0.1%
30.35569807 1
< 0.1%
30.7282061 1
< 0.1%
30.73048397 1
< 0.1%
30.74439908 1
< 0.1%
30.84378548 1
< 0.1%
30.9591821 1
< 0.1%
30.97033041 1
< 0.1%
30.97110544 1
< 0.1%
31.00340979 1
< 0.1%
ValueCountFrequency (%)
106.7482648 1
< 0.1%
89.47997004 1
< 0.1%
86.78711363 1
< 0.1%
79.52256125 1
< 0.1%
76.65375682 1
< 0.1%
75.31273657 1
< 0.1%
73.4691458 1
< 0.1%
73.13808881 1
< 0.1%
68.87363018 1
< 0.1%
67.08390361 1
< 0.1%

hrstart
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.18604
Minimum0
Maximum23
Zeros1153
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size165.0 KiB
2024-10-09T15:07:18.088243image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median14
Q319
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)15

Descriptive statistics

Standard deviation7.848888
Coefficient of variation (CV)0.64408849
Kurtosis-1.4826981
Mean12.18604
Median Absolute Deviation (MAD)7
Skewness-0.24444364
Sum257162
Variance61.605043
MonotonicityNot monotonic
2024-10-09T15:07:18.412820image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
3 1481
 
7.0%
23 1365
 
6.5%
2 1362
 
6.5%
14 1312
 
6.2%
15 1304
 
6.2%
4 1227
 
5.8%
18 1169
 
5.5%
0 1153
 
5.5%
19 1143
 
5.4%
17 1140
 
5.4%
Other values (14) 8447
40.0%
ValueCountFrequency (%)
0 1153
5.5%
1 1127
5.3%
2 1362
6.5%
3 1481
7.0%
4 1227
5.8%
5 843
4.0%
6 483
 
2.3%
7 150
 
0.7%
8 101
 
0.5%
9 24
 
0.1%
ValueCountFrequency (%)
23 1365
6.5%
22 1114
5.3%
21 1122
5.3%
20 1109
5.3%
19 1143
5.4%
18 1169
5.5%
17 1140
5.4%
16 998
4.7%
15 1304
6.2%
14 1312
6.2%

distapt
Real number (ℝ)

HIGH CORRELATION 

Distinct20974
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean214.06982
Minimum190.63084
Maximum239.6557
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size165.0 KiB
2024-10-09T15:07:18.795394image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum190.63084
5-th percentile199.11331
Q1202.05281
median214.58231
Q3226.4519
95-th percentile235.61538
Maximum239.6557
Range49.024861
Interquartile range (IQR)24.399089

Descriptive statistics

Standard deviation12.641882
Coefficient of variation (CV)0.059054948
Kurtosis-1.1717278
Mean214.06982
Median Absolute Deviation (MAD)12.170367
Skewness0.36591698
Sum4517515.5
Variance159.81719
MonotonicityNot monotonic
2024-10-09T15:07:19.234585image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
205.6164846 3
 
< 0.1%
199.677132 3
 
< 0.1%
201.8056157 2
 
< 0.1%
224.2541233 2
 
< 0.1%
226.8969343 2
 
< 0.1%
206.3944691 2
 
< 0.1%
199.4760571 2
 
< 0.1%
205.6773785 2
 
< 0.1%
199.3338501 2
 
< 0.1%
206.2497057 2
 
< 0.1%
Other values (20964) 21081
99.9%
ValueCountFrequency (%)
190.6308418 1
< 0.1%
191.0247307 1
< 0.1%
191.1164989 1
< 0.1%
191.2247616 1
< 0.1%
191.5548098 1
< 0.1%
191.5673339 1
< 0.1%
191.6335508 1
< 0.1%
191.7748041 1
< 0.1%
191.8835506 1
< 0.1%
192.0324036 1
< 0.1%
ValueCountFrequency (%)
239.655703 1
< 0.1%
239.6400802 1
< 0.1%
239.6316843 1
< 0.1%
239.6306094 1
< 0.1%
239.6288354 1
< 0.1%
239.6262386 1
< 0.1%
239.6126801 1
< 0.1%
239.6067415 1
< 0.1%
239.6027732 1
< 0.1%
239.598043 1
< 0.1%

flightLevel
Real number (ℝ)

Distinct963
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean331.14812
Minimum18.75
Maximum470
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size165.0 KiB
2024-10-09T15:07:19.659896image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum18.75
5-th percentile209.75
Q1320
median350
Q3370
95-th percentile400
Maximum470
Range451.25
Interquartile range (IQR)50

Descriptive statistics

Standard deviation73.185342
Coefficient of variation (CV)0.22100486
Kurtosis8.4123612
Mean331.14812
Median Absolute Deviation (MAD)20
Skewness-2.6858723
Sum6988218.8
Variance5356.0943
MonotonicityNot monotonic
2024-10-09T15:07:20.061934image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
350 2741
 
13.0%
370 2178
 
10.3%
360 1794
 
8.5%
380 1375
 
6.5%
340 1287
 
6.1%
390 1033
 
4.9%
400 576
 
2.7%
330 511
 
2.4%
320 457
 
2.2%
410 373
 
1.8%
Other values (953) 8778
41.6%
ValueCountFrequency (%)
18.75 1
 
< 0.1%
19 1
 
< 0.1%
19.25 5
 
< 0.1%
19.5 11
 
0.1%
19.75 17
 
0.1%
20 28
 
0.1%
20.25 31
 
0.1%
20.5 57
0.3%
20.75 76
0.4%
21 86
0.4%
ValueCountFrequency (%)
470 10
 
< 0.1%
450.5 1
 
< 0.1%
450 30
0.1%
449.75 7
 
< 0.1%
449.5 1
 
< 0.1%
430.25 2
 
< 0.1%
430 31
0.1%
429.75 5
 
< 0.1%
428.5 1
 
< 0.1%
420 2
 
< 0.1%

emitterCat
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing84
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean3.3640516
Minimum0
Maximum5
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size165.0 KiB
2024-10-09T15:07:20.375540image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q13
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.83676458
Coefficient of variation (CV)0.24873714
Kurtosis0.25190669
Mean3.3640516
Median Absolute Deviation (MAD)0
Skewness1.1629604
Sum70709
Variance0.70017496
MonotonicityNot monotonic
2024-10-09T15:07:20.924135image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 15698
74.4%
5 4013
 
19.0%
2 780
 
3.7%
4 489
 
2.3%
1 34
 
0.2%
0 5
 
< 0.1%
(Missing) 84
 
0.4%
ValueCountFrequency (%)
0 5
 
< 0.1%
1 34
 
0.2%
2 780
 
3.7%
3 15698
74.4%
4 489
 
2.3%
5 4013
 
19.0%
ValueCountFrequency (%)
5 4013
 
19.0%
4 489
 
2.3%
3 15698
74.4%
2 780
 
3.7%
1 34
 
0.2%
0 5
 
< 0.1%

airGroundVector_groundSpeed
Real number (ℝ)

HIGH CORRELATION 

Distinct1123
Distinct (%)5.3%
Missing7
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean440.2875
Minimum61.962891
Maximum571.06934
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size165.0 KiB
2024-10-09T15:07:21.275130image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum61.962891
5-th percentile373.0957
Q1429.3457
median449.34082
Q3469.99512
95-th percentile505.37109
Maximum571.06934
Range509.10645
Interquartile range (IQR)40.649414

Descriptive statistics

Standard deviation65.598824
Coefficient of variation (CV)0.14899088
Kurtosis13.243234
Mean440.2875
Median Absolute Deviation (MAD)20.43457
Skewness-3.3010907
Sum9288305.2
Variance4303.2057
MonotonicityNot monotonic
2024-10-09T15:07:21.764353image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
457.4707031 110
 
0.5%
439.6728516 110
 
0.5%
448.9013672 98
 
0.5%
449.5605469 96
 
0.5%
446.7041016 92
 
0.4%
461.6455078 89
 
0.4%
437.6953125 87
 
0.4%
442.0898438 86
 
0.4%
442.96875 86
 
0.4%
439.8925781 86
 
0.4%
Other values (1113) 20156
95.5%
ValueCountFrequency (%)
61.96289062 1
< 0.1%
65.91796875 1
< 0.1%
69.87304688 1
< 0.1%
73.828125 1
< 0.1%
74.92675781 1
< 0.1%
76.90429688 2
< 0.1%
77.78320312 1
< 0.1%
82.83691406 1
< 0.1%
83.93554688 1
< 0.1%
88.98925781 2
< 0.1%
ValueCountFrequency (%)
571.0693359 1
< 0.1%
570.8496094 1
< 0.1%
568.8720703 1
< 0.1%
567.7734375 1
< 0.1%
567.3339844 1
< 0.1%
565.1367188 1
< 0.1%
564.2578125 1
< 0.1%
560.9619141 1
< 0.1%
560.0830078 1
< 0.1%
559.8632812 1
< 0.1%

calculated_time
Real number (ℝ)

HIGH CORRELATION 

Distinct21096
Distinct (%)100.0%
Missing7
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean30.866479
Minimum20.939816
Maximum199.39124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size165.0 KiB
2024-10-09T15:07:22.176432image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum20.939816
5-th percentile24.788596
Q126.520102
median28.220305
Q330.95077
95-th percentile35.927872
Maximum199.39124
Range178.45142
Interquartile range (IQR)4.4306681

Descriptive statistics

Standard deviation12.279866
Coefficient of variation (CV)0.39783825
Kurtosis30.759957
Mean30.866479
Median Absolute Deviation (MAD)2.0636169
Skewness5.2600058
Sum651159.24
Variance150.79511
MonotonicityNot monotonic
2024-10-09T15:07:22.586234image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.85938342 1
 
< 0.1%
26.08497353 1
 
< 0.1%
27.34765578 1
 
< 0.1%
28.99179504 1
 
< 0.1%
25.68684526 1
 
< 0.1%
26.33164334 1
 
< 0.1%
26.83582353 1
 
< 0.1%
25.96067705 1
 
< 0.1%
27.51063706 1
 
< 0.1%
29.45045796 1
 
< 0.1%
Other values (21086) 21086
99.9%
(Missing) 7
 
< 0.1%
ValueCountFrequency (%)
20.9398163 1
< 0.1%
21.3158781 1
< 0.1%
21.4781371 1
< 0.1%
21.4879273 1
< 0.1%
21.5374657 1
< 0.1%
21.55341601 1
< 0.1%
21.6702647 1
< 0.1%
21.67337249 1
< 0.1%
21.67754009 1
< 0.1%
21.71644292 1
< 0.1%
ValueCountFrequency (%)
199.3912361 1
< 0.1%
187.4957132 1
< 0.1%
175.8955064 1
< 0.1%
166.4799851 1
< 0.1%
164.9032959 1
< 0.1%
160.6294636 1
< 0.1%
159.8172501 1
< 0.1%
158.7940672 1
< 0.1%
149.1560004 1
< 0.1%
147.1894824 1
< 0.1%

h3_id
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
8229affffffffff
9248 
8229b7fffffffff
7005 
82298ffffffffff
1929 
82291ffffffffff
1086 
822987fffffffff
1028 
Other values (3)
 
807

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters316545
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8229b7fffffffff
2nd row8229affffffffff
3rd row8229b7fffffffff
4th row8229affffffffff
5th row8229b7fffffffff

Common Values

ValueCountFrequency (%)
8229affffffffff 9248
43.8%
8229b7fffffffff 7005
33.2%
82298ffffffffff 1929
 
9.1%
82291ffffffffff 1086
 
5.1%
822987fffffffff 1028
 
4.9%
822917fffffffff 684
 
3.2%
82485ffffffffff 67
 
0.3%
822937fffffffff 56
 
0.3%

Length

2024-10-09T15:07:22.929682image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-09T15:07:23.280582image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
8229affffffffff 9248
43.8%
8229b7fffffffff 7005
33.2%
82298ffffffffff 1929
 
9.1%
82291ffffffffff 1086
 
5.1%
822987fffffffff 1028
 
4.9%
822917fffffffff 684
 
3.2%
82485ffffffffff 67
 
0.3%
822937fffffffff 56
 
0.3%

Most occurring characters

ValueCountFrequency (%)
f 202257
63.9%
2 42139
 
13.3%
8 24127
 
7.6%
9 21036
 
6.6%
a 9248
 
2.9%
7 8773
 
2.8%
b 7005
 
2.2%
1 1770
 
0.6%
4 67
 
< 0.1%
5 67
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 316545
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
f 202257
63.9%
2 42139
 
13.3%
8 24127
 
7.6%
9 21036
 
6.6%
a 9248
 
2.9%
7 8773
 
2.8%
b 7005
 
2.2%
1 1770
 
0.6%
4 67
 
< 0.1%
5 67
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 316545
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
f 202257
63.9%
2 42139
 
13.3%
8 24127
 
7.6%
9 21036
 
6.6%
a 9248
 
2.9%
7 8773
 
2.8%
b 7005
 
2.2%
1 1770
 
0.6%
4 67
 
< 0.1%
5 67
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 316545
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
f 202257
63.9%
2 42139
 
13.3%
8 24127
 
7.6%
9 21036
 
6.6%
a 9248
 
2.9%
7 8773
 
2.8%
b 7005
 
2.2%
1 1770
 
0.6%
4 67
 
< 0.1%
5 67
 
< 0.1%

temp
Real number (ℝ)

HIGH CORRELATION 

Distinct82
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.917874
Minimum15.6
Maximum28.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size165.0 KiB
2024-10-09T15:07:23.678917image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum15.6
5-th percentile17.2
Q119
median21
Q322.8
95-th percentile25
Maximum28.3
Range12.7
Interquartile range (IQR)3.8

Descriptive statistics

Standard deviation2.3098092
Coefficient of variation (CV)0.11042275
Kurtosis-0.41081986
Mean20.917874
Median Absolute Deviation (MAD)1.8
Skewness0.20104769
Sum441429.9
Variance5.3352184
MonotonicityNot monotonic
2024-10-09T15:07:24.046447image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.4 1575
 
7.5%
20 1405
 
6.7%
18.9 1366
 
6.5%
21.1 1349
 
6.4%
21.7 1285
 
6.1%
22.2 1158
 
5.5%
20.6 1104
 
5.2%
22.8 1094
 
5.2%
23.3 940
 
4.5%
17.8 840
 
4.0%
Other values (72) 8987
42.6%
ValueCountFrequency (%)
15.6 65
 
0.3%
16 15
 
0.1%
16.1 156
 
0.7%
16.7 345
1.6%
17 155
 
0.7%
17.2 555
2.6%
17.3 4
 
< 0.1%
17.8 840
4.0%
17.9 1
 
< 0.1%
18 397
1.9%
ValueCountFrequency (%)
28.3 17
 
0.1%
28 21
 
0.1%
27.8 21
 
0.1%
27.2 64
 
0.3%
27 44
 
0.2%
26.7 35
 
0.2%
26.1 170
0.8%
26 49
 
0.2%
25.6 174
0.8%
25.2 23
 
0.1%

dwpt
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct59
Distinct (%)0.3%
Missing234
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean16.480799
Minimum12.3
Maximum19.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size165.0 KiB
2024-10-09T15:07:24.397306image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum12.3
5-th percentile14.7
Q115.7
median16.5
Q317.2
95-th percentile18.3
Maximum19.5
Range7.2
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.1278213
Coefficient of variation (CV)0.068432438
Kurtosis-0.17632743
Mean16.480799
Median Absolute Deviation (MAD)0.8
Skewness0.025538247
Sum343937.8
Variance1.2719808
MonotonicityNot monotonic
2024-10-09T15:07:24.830269image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.1 2002
 
9.5%
15.6 1518
 
7.2%
16.6 1329
 
6.3%
17.2 1259
 
6.0%
16.7 1227
 
5.8%
17.3 975
 
4.6%
16 870
 
4.1%
15.7 774
 
3.7%
15 746
 
3.5%
17.8 678
 
3.2%
Other values (49) 9491
45.0%
ValueCountFrequency (%)
12.3 21
 
0.1%
12.9 18
 
0.1%
13.1 4
 
< 0.1%
13.3 23
 
0.1%
13.5 27
 
0.1%
13.7 29
 
0.1%
13.9 96
0.5%
14 110
0.5%
14.1 61
 
0.3%
14.3 162
0.8%
ValueCountFrequency (%)
19.5 17
 
0.1%
19.4 9
 
< 0.1%
19.3 36
 
0.2%
19.2 60
 
0.3%
19 155
0.7%
18.9 172
0.8%
18.8 194
0.9%
18.7 17
 
0.1%
18.6 22
 
0.1%
18.4 266
1.3%

rhum
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct41
Distinct (%)0.2%
Missing234
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean76.439456
Minimum51
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size165.0 KiB
2024-10-09T15:07:25.196315image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum51
5-th percentile62
Q170
median76
Q384
95-th percentile93
Maximum100
Range49
Interquartile range (IQR)14

Descriptive statistics

Standard deviation9.0705411
Coefficient of variation (CV)0.11866308
Kurtosis-0.65145645
Mean76.439456
Median Absolute Deviation (MAD)7
Skewness0.16336521
Sum1595215
Variance82.274715
MonotonicityNot monotonic
2024-10-09T15:07:25.654381image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
71 1960
 
9.3%
73 1811
 
8.6%
84 1683
 
8.0%
81 1587
 
7.5%
87 1577
 
7.5%
76 1505
 
7.1%
90 1156
 
5.5%
78 1043
 
4.9%
69 1041
 
4.9%
66 995
 
4.7%
Other values (31) 6511
30.9%
ValueCountFrequency (%)
51 17
 
0.1%
55 20
 
0.1%
56 48
 
0.2%
57 20
 
0.1%
58 80
 
0.4%
59 98
 
0.5%
60 389
1.8%
61 171
0.8%
62 398
1.9%
63 71
 
0.3%
ValueCountFrequency (%)
100 32
 
0.2%
97 215
 
1.0%
96 181
 
0.9%
93 628
 
3.0%
91 77
 
0.4%
90 1156
5.5%
89 4
 
< 0.1%
87 1577
7.5%
86 89
 
0.4%
85 16
 
0.1%

wdir
Real number (ℝ)

ZEROS 

Distinct84
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean238.6918
Minimum0
Maximum360
Zeros873
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size165.0 KiB
2024-10-09T15:07:26.088156image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile80
Q1247
median258
Q3260
95-th percentile280
Maximum360
Range360
Interquartile range (IQR)13

Descriptive statistics

Standard deviation62.114756
Coefficient of variation (CV)0.26022996
Kurtosis7.4910722
Mean238.6918
Median Absolute Deviation (MAD)8
Skewness-2.7914796
Sum5037113
Variance3858.2429
MonotonicityNot monotonic
2024-10-09T15:07:26.565207image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
260 5068
24.0%
250 3733
17.7%
270 3371
16.0%
240 1873
 
8.9%
0 873
 
4.1%
230 643
 
3.0%
280 569
 
2.7%
256 331
 
1.6%
258 302
 
1.4%
259 296
 
1.4%
Other values (74) 4044
19.2%
ValueCountFrequency (%)
0 873
4.1%
10 1
 
< 0.1%
15 20
 
0.1%
20 17
 
0.1%
30 5
 
< 0.1%
40 24
 
0.1%
60 2
 
< 0.1%
62 6
 
< 0.1%
70 4
 
< 0.1%
75 15
 
0.1%
ValueCountFrequency (%)
360 36
 
0.2%
350 36
 
0.2%
340 20
 
0.1%
337 4
 
< 0.1%
332 8
 
< 0.1%
330 36
 
0.2%
320 157
0.7%
310 28
 
0.1%
300 135
0.6%
290 144
0.7%

wspd
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct39
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.61777
Minimum0
Maximum30
Zeros839
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size165.0 KiB
2024-10-09T15:07:26.948450image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.4
Q19.4
median14.8
Q320.5
95-th percentile24.1
Maximum30
Range30
Interquartile range (IQR)11.1

Descriptive statistics

Standard deviation6.5418091
Coefficient of variation (CV)0.44752443
Kurtosis-0.6430663
Mean14.61777
Median Absolute Deviation (MAD)5.4
Skewness-0.20225532
Sum308478.8
Variance42.795267
MonotonicityNot monotonic
2024-10-09T15:07:27.298965image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
13 1828
 
8.7%
20.5 1733
 
8.2%
18.4 1663
 
7.9%
14.8 1520
 
7.2%
16.6 1397
 
6.6%
7.6 1380
 
6.5%
22.3 1238
 
5.9%
11.2 1232
 
5.8%
9.4 1226
 
5.8%
5.4 1056
 
5.0%
Other values (29) 6830
32.4%
ValueCountFrequency (%)
0 839
4.0%
1.8 3
 
< 0.1%
3.7 28
 
0.1%
5.4 1056
5.0%
5.5 132
 
0.6%
6 385
 
1.8%
7 250
 
1.2%
7.4 251
 
1.2%
7.6 1380
6.5%
9 221
 
1.0%
ValueCountFrequency (%)
30 48
 
0.2%
29.5 23
 
0.1%
28 84
 
0.4%
27.8 42
 
0.2%
27.7 88
 
0.4%
26 85
 
0.4%
25.9 364
 
1.7%
24.1 805
3.8%
24 252
 
1.2%
22.3 1238
5.9%

pres
Real number (ℝ)

Distinct102
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1013.3384
Minimum1007.6
Maximum1018.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size165.0 KiB
2024-10-09T15:07:27.655404image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1007.6
5-th percentile1010.1
Q11012
median1013.4
Q31014.7
95-th percentile1016.9
Maximum1018.3
Range10.7
Interquartile range (IQR)2.7

Descriptive statistics

Standard deviation1.9863116
Coefficient of variation (CV)0.0019601662
Kurtosis-0.31735568
Mean1013.3384
Median Absolute Deviation (MAD)1.4
Skewness0.0063491509
Sum21384480
Variance3.945434
MonotonicityNot monotonic
2024-10-09T15:07:28.104089image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1014 1359
 
6.4%
1013 1235
 
5.9%
1012 672
 
3.2%
1015 671
 
3.2%
1011 577
 
2.7%
1014.1 492
 
2.3%
1013.3 487
 
2.3%
1013.6 467
 
2.2%
1013.7 452
 
2.1%
1012.1 381
 
1.8%
Other values (92) 14310
67.8%
ValueCountFrequency (%)
1007.6 15
 
0.1%
1007.7 42
0.2%
1008 39
0.2%
1008.2 7
 
< 0.1%
1008.4 28
0.1%
1008.6 16
 
0.1%
1008.7 13
 
0.1%
1008.8 33
0.2%
1008.9 3
 
< 0.1%
1009 40
0.2%
ValueCountFrequency (%)
1018.3 15
 
0.1%
1018.2 38
 
0.2%
1018 100
0.5%
1017.9 39
 
0.2%
1017.8 35
 
0.2%
1017.7 46
0.2%
1017.6 114
0.5%
1017.5 82
0.4%
1017.4 71
0.3%
1017.3 14
 
0.1%

coco
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
2.0
14356 
3.0
3062 
5.0
2951 
1.0
 
407
4.0
 
327

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters63309
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row5.0
3rd row5.0
4th row5.0
5th row5.0

Common Values

ValueCountFrequency (%)
2.0 14356
68.0%
3.0 3062
 
14.5%
5.0 2951
 
14.0%
1.0 407
 
1.9%
4.0 327
 
1.5%

Length

2024-10-09T15:07:28.785489image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-09T15:07:29.310585image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2.0 14356
68.0%
3.0 3062
 
14.5%
5.0 2951
 
14.0%
1.0 407
 
1.9%
4.0 327
 
1.5%

Most occurring characters

ValueCountFrequency (%)
. 21103
33.3%
0 21103
33.3%
2 14356
22.7%
3 3062
 
4.8%
5 2951
 
4.7%
1 407
 
0.6%
4 327
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63309
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 21103
33.3%
0 21103
33.3%
2 14356
22.7%
3 3062
 
4.8%
5 2951
 
4.7%
1 407
 
0.6%
4 327
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63309
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 21103
33.3%
0 21103
33.3%
2 14356
22.7%
3 3062
 
4.8%
5 2951
 
4.7%
1 407
 
0.6%
4 327
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63309
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 21103
33.3%
0 21103
33.3%
2 14356
22.7%
3 3062
 
4.8%
5 2951
 
4.7%
1 407
 
0.6%
4 327
 
0.5%

Interactions

2024-10-09T15:07:10.052548image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:30.245581image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:34.425118image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:37.329092image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:40.559854image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:44.955096image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:48.119479image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:51.328421image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:54.441113image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:57.641965image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:00.642495image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:03.502608image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:07.019282image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:10.292441image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:30.935660image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:34.669556image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:37.562472image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:40.827582image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:45.198524image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:48.358982image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:51.594347image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:54.704434image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:57.894060image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:00.882011image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:03.952001image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:07.294427image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:10.516706image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:31.315327image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:34.885593image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:37.762515image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:41.085253image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:45.410656image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:48.577759image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:51.829297image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:54.939200image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:58.106411image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:01.089968image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:04.364289image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:07.510524image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:10.739385image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:31.933034image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:35.097544image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:37.966514image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:41.332669image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:45.667069image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:48.831859image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:52.065650image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:55.160239image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:58.328436image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:01.295341image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:04.589427image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:07.745294image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:10.949349image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:32.236548image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:35.284508image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:38.184701image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:41.585772image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:45.908782image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:49.048443image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:52.278601image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:55.398314image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:58.541849image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:01.505496image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:04.806990image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:07.974864image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:11.162333image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:32.485201image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:35.492358image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:38.397459image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:41.917425image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:46.133135image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:49.270720image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:52.517294image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:55.660665image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:58.763798image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:01.717925image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:05.027952image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:08.201670image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:11.372820image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:32.726926image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:35.697150image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:38.617760image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:42.572209image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:46.339139image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:49.482413image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:52.753422image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:55.873437image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:58.980101image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:01.924250image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:05.357599image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:08.446200image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:11.612282image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:32.981365image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:35.914812image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:38.850999image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:43.110369image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:46.611363image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:49.744327image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:53.008507image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:56.133631image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:59.226015image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:02.156605image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:05.599397image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:08.684263image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:11.826407image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:33.217976image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:36.272906image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:39.078889image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:43.489040image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:46.867717image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:49.981514image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:53.233478image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:56.378268image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:59.456433image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:02.374622image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:05.835110image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:08.925299image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:12.159901image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:33.470489image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:36.493869image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:39.403636image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:43.827592image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:47.141129image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:50.208901image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:53.486980image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:56.654071image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:59.687794image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:02.610258image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:06.065346image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:09.158971image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:12.652600image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:33.728416image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:36.698376image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:39.702197image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:44.103244image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:47.409550image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:50.422030image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:53.719347image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:56.895609image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:59.903300image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:02.826071image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:06.284460image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:09.382579image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:13.055720image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:33.965257image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:36.921868image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:39.995232image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:44.436805image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:47.662813image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:50.678042image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:53.984403image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:57.142907image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:00.169153image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:03.063601image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:06.528613image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:09.617107image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:13.386041image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:34.195714image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:37.130128image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:40.304296image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:44.712060image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:47.900391image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:51.117552image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:54.214845image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:06:57.416584image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:00.413339image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:03.282229image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:06.772738image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T15:07:09.834463image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-10-09T15:07:29.563203image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ACTypeAC_roleairGroundVector_groundSpeedcalculated_timecocodistaptdwptemitterCatflightLevelh3_idhrstartpresrhumtempttlandwdirwspd
ACType1.0000.6930.2640.0930.0460.3040.0250.7720.3240.2910.1960.0230.0690.0700.1820.0410.076
AC_role0.6931.0000.1130.0810.0180.0550.0230.3110.2690.0870.1980.0210.0590.0620.0640.0470.061
airGroundVector_groundSpeed0.2640.1131.000-0.8090.048-0.130-0.0500.4770.3420.360-0.048-0.0600.071-0.083-0.2360.016-0.027
calculated_time0.0930.081-0.8091.0000.0080.6390.050-0.463-0.3550.3120.0920.039-0.0920.1010.6570.0090.057
coco0.0460.0180.0480.0081.0000.0320.1360.0160.0260.0350.2200.1470.3150.2560.0560.1670.172
distapt0.3040.055-0.1300.6390.0321.0000.032-0.139-0.0660.5720.1120.006-0.0820.0880.8110.0310.074
dwpt0.0250.023-0.0500.0500.1360.0321.000-0.021-0.0220.0280.231-0.031-0.0520.5150.015-0.065-0.014
emitterCat0.7720.3110.477-0.4630.016-0.139-0.0211.0000.4440.1290.022-0.013-0.0300.016-0.1600.0110.036
flightLevel0.3240.2690.342-0.3550.026-0.066-0.0220.4441.0000.3550.0320.013-0.0400.027-0.1760.0010.042
h3_id0.2910.0870.3600.3120.0350.5720.0280.1290.3551.0000.1780.0280.0670.0680.3790.0370.050
hrstart0.1960.198-0.0480.0920.2200.1120.2310.0220.0320.1781.0000.172-0.5240.5610.0630.1120.274
pres0.0230.021-0.0600.0390.1470.006-0.031-0.0130.0130.0280.1721.000-0.1420.118-0.034-0.075-0.128
rhum0.0690.0590.071-0.0920.315-0.082-0.052-0.030-0.0400.067-0.524-0.1421.000-0.8660.003-0.123-0.547
temp0.0700.062-0.0830.1010.2560.0880.5150.0160.0270.0680.5610.118-0.8661.0000.0060.0600.455
ttland0.1820.064-0.2360.6570.0560.8110.015-0.160-0.1760.3790.063-0.0340.0030.0061.0000.0260.053
wdir0.0410.0470.0160.0090.1670.031-0.0650.0110.0010.0370.112-0.075-0.1230.0600.0261.0000.393
wspd0.0760.061-0.0270.0570.1720.074-0.0140.0360.0420.0500.274-0.128-0.5470.4550.0530.3931.000

Missing values

2024-10-09T15:07:13.933819image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-09T15:07:14.663922image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-10-09T15:07:15.330362image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ACTypeAC_rolettlandhrstartdistaptflightLevelemitterCatairGroundVector_groundSpeedcalculated_timeh3_idtempdwptrhumwdirwspdprescoco
0A321-200PAX39.5868820206.102875339.753.0419.89746129.4504588229b7fffffffff17.815.184.0270.018.41012.15.0
1E175PAX43.9027200219.248186206.253.0392.21191433.5402648229affffffffff17.815.184.0270.018.41012.15.0
2A321-200PAX37.2273030205.968685320.003.0427.58789128.9019448229b7fffffffff17.815.184.0270.018.41012.15.0
3A320-200PAX41.7663060219.155811229.503.0445.38574229.5235068229affffffffff17.815.184.0270.018.41012.15.0
4B737-700PAX39.4495350201.120239360.003.0406.49414129.6860728229b7fffffffff17.815.184.0270.018.41012.15.0
5Not FoundNot Found44.8478520227.006608409.752.0469.77539128.9934238229affffffffff17.815.184.0270.018.41012.15.0
6B737-800PAX51.3679670228.036874350.003.0483.17871128.3170858229affffffffff17.815.184.0270.018.41012.15.0
7E175PAX35.9347290198.479053276.503.0439.67285227.0854648229b7fffffffff17.815.184.0270.018.41012.15.0
8A319-100PAX46.7091770226.808320370.253.0457.03125029.7758628229affffffffff17.815.184.0270.018.41012.15.0
9E175PAX47.0580340235.793836330.003.0459.88769530.76322882298ffffffffff17.815.184.0270.018.41012.15.0
ACTypeAC_rolettlandhrstartdistaptflightLevelemitterCatairGroundVector_groundSpeedcalculated_timeh3_idtempdwptrhumwdirwspdprescoco
21093E175PAX43.01823422233.320779370.003.0454.39453130.8085728229affffffffff22.115.968.0258.022.21012.62.0
21094B737-800PAX33.68104022199.827281356.503.0451.53808626.5528818229b7fffffffff22.115.968.0258.022.21012.62.0
21095A340-300PAX38.34326123209.320012360.005.0421.65527329.785470822987fffffffff21.615.769.0261.025.91012.42.0
21096A321-200PAX38.58875623208.223071360.003.0442.30957028.245792822987fffffffff21.615.769.0261.025.91012.42.0
21097B787-9PAX37.84289823208.812302399.755.0457.91015627.360691822987fffffffff21.615.769.0261.025.91012.42.0
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